Simona Bassu

How do various maize crop models vary in their responses to climate change factors

Potential consequences of climate change on crop production can be studied using mechanistic crop simulation models. While a broad variety of maize simulation models exist, it is not known whether different models diverge on grain yield responses to changes in climatic factors, or whether they agree in their general trends related to phenology, growth, and yield. With the goal of analyzing the sensitivity of simulated yields to changes in temperature and atmospheric carbon dioxide concentrations [CO2 ], we present the largest maize crop model intercomparison to date, including 23 different models. These models were evaluated for four locations representing a wide range of maize production conditions in the world: Lusignan (France), Ames (USA), Rio Verde (Brazil) and Morogoro (Tanzania). While individual models differed considerably in absolute yield simulation at the four sites, an ensemble of a minimum number of models was able to simulate absolute yields accurately at the four sites even with low data for calibration, thus suggesting that using an ensemble of models has merit. Temperature increase had strong negative influence on modeled yield response of roughly -0.5 Mg ha(-1) per °C. Doubling [CO2 ] from 360 to 720 μmol mol(-1) increased grain yield by 7.5% on average across models and the sites. That would therefore make temperature the main factor altering maize yields at the end of this century. Furthermore, there was a large uncertainty in the yield response to [CO2 ] among models. Model responses to temperature and [CO2 ] did not differ whether models were simulated with low calibration information or, simulated with high level of calibration information.

Temperature increase reduces global yields of major crops in four independent estimates

Significance Agricultural production is vulnerable to climate change. Understanding climate change, especially the temperature impacts, is critical if policymakers, agriculturalists, and crop breeders are to ensure global food security. Our study, by compiling extensive published results from four analytical methods, shows that independent methods consistently estimated negative temperature impacts on yields of four major crops at the global scale, generally underpinned by similar impacts at country and site scales. Multimethod analyses improved the confidence in assessments of future climate impacts on global major crops, with important implications for developing crop- and region-specific adaptation strategies to ensure future food supply of an increasing world population. Wheat, rice, maize, and soybean provide two-thirds of human caloric intake. Assessing the impact of global temperature increase on production of these crops is therefore critical to maintaining global food supply, but different studies have yielded different results. Here, we investigated the impacts of temperature on yields of the four crops by compiling extensive published results from four analytical methods: global grid-based and local point-based models, statistical regressions, and field-warming experiments. Results from the different methods consistently showed negative temperature impacts on crop yield at the global scale, generally underpinned by similar impacts at country and site scales. Without CO2 fertilization, effective adaptation, and genetic improvement, each degree-Celsius increase in global mean temperature would, on average, reduce global yields of wheat by 6.0%, rice by 3.2%, maize by 7.4%, and soybean by 3.1%. Results are highly heterogeneous across crops and geographical areas, with some positive impact estimates. Multimethod analyses improved the confidence in assessments of future climate impacts on global major crops and suggest crop- and region-specific adaptation strategies to ensure food security for an increasing world population.

Effects of sowing date and cultivar on spike weight and kernel number in durum wheat

In wheat, spike weight is associated with kernel number. The response of spike weight to photoperiod and the amount of radiation available during the period of spike growth and the associated changes in spike : stem ratio were investigated through field trials involving three durum wheat cultivars with different flowering time over two seasons and three sowing dates. Across the three cultivars spike and stem weight differed in response to the photoperiod and to the photothermal quotient, i.e. the ratio between intercepted radiation and temperature; this reflected the sensitivity of the spike : stem ratio to the environmental conditions induced by sowing date, which affected the allometry of the ratio. The photothermal quotient (0.14–1.70 MJ m–2 day–1 °C–1) explained most of the variation in both spike weight (83–270 g m–2) and kernel number per m2 (2638–13 993), across all the environments sampled. The phenology explained a significant portion of the variation in spike weight, but its influence was minor compared with the combined effects of the quantity of intercepted radiation and the temperature. Therefore, the correlation between kernel number and the photothermal quotient before anthesis was more sensible to the environmental variation induced by sowing date beyond its conventional window.

Effects of sowing date and cultivar on radiation use efficiency in durum wheat

Field studies were conducted on durum wheat to assess the effects of three sowing dates and three cultivars with different flowering times on the stability of the biomass accumulated per unit of solar radiation intercepted that is usually considered constant in crop-simulation models. Aboveground dry matter varied widely, with minimum values ranging from 292 g m–2 at booting to 384 g m–2 at maturity and maximum values ranging from 1452 g m–2 at booting to 2565 g m–2 at maturity. The cumulative intercepted radiation at each phenological stage decreased as sowing was delayed. The leaf area index (LAI) ranged from 1.5 to 7.6 at booting and from 0.1 to 4.6 at the beginning of grain filling across treatments. Sowing dates and cultivars did not differ significantly in extinction coefficient values (0.38 ± 0.015). The estimated radiation use efficiency (eRUE) differed significantly between the two seasons (1.16 ± 0.09 g MJ–1 in 2000 and 1.61 ± 0.08 g MJ–1 in 2001) due to waterlogging in 2000 but did not differ among sowing dates and cultivars within each season. Under optimal growing conditions, eRUE of different cultivars of durum wheat were relatively stable across sowing dates, confirming their reliability for crop modelling in durum wheat as well as in bread wheat. Although eRUE was constant over the whole crop cycle regardless of the sowing date, it was lower at pre-anthesis in the latest sowing, in parallel with the variation in LAI. This study indicates that pre-anthesis eRUE may vary with sowing date under some conditions, depending on the variation in LAI in the period before anthesis.

Variation for kernel number and related traits in triticale (x Triticosecale Wittmack)

Assessing the existence and extent of genetic variation in kernel number per m2 (KNO) and in KNO-related traits is necessary both for overcoming sink limitations through breeding and in order to correctly model triticale grain yield. A set of 112 advanced breeding lines derived from various crosses between winter and spring hexaploid triticales was grown for 2 years in a field experiment to evaluate genetic variation and heritability for KNO, chaff weight per m2 at maturity (CHAFFW) and number of kernels per unit weight of chaff (K/CHAFF). Genetic correlations were also calculated between these traits and grain weight and yield. K/CHAFF (but not CHAFFW) exhibited a high level of genetic variation and a low contribution of the genotype by environment interaction component to the overall variance and was highly heritable. There was no detectable genetic correlation between K/CHAFF and CHAFFW; however, K/CHAFF was correlated with KNO (r = 0.66, P < 0.001). K/CHAFF fulfils the major requirements of an indirect screening trait for KNO and of a genetic coefficient in modelling.

Statistical Analysis of Large Simulated Yield Datasets for Studying Climate Effects

Many simulation studies have been carried out to predict the effect of climate change on crop yield. Typically, in such study, one or several crop models are used to simulate series of crop yield values for different climate scenarios corresponding to different hypotheses of temperature, CO2 concentration, and rainfall changes. These studies usually generate large datasets including thousands of simulated yield data. The structure of these datasets is complex because they include series of yield values obtained with different mechanistic crop models for different climate scenarios defined from several climatic variables (temperature, CO2 etc.). Statistical methods can play a big part for analyzing large simulated crop yield datasets, especially when yields are simulated using an ensemble of crop models. A formal statistical analysis is then needed in order to estimate the effects of different climatic variables on yield, and to describe the variability of these effects across crop models. Statistical methods are also useful to develop meta-models i.e., statistical models summarizing complex mechanistic models. The objective of this paper is to present a random-coefficient statistical model (mixed-effects model) for analyzing large simulated crop yield datasets produced by the international project AgMip for several major crops. The proposed statistical model shows several interesting features; i) it can be used to estimate the effects of several climate variables on yield using crop model simulations, ii) it quantities the variability of the estimated climate change effects across crop models, ii) it quantifies the between-year yield variability, iv) it can be used as a meta-model in order to estimate effects of new climate change scenarios without running again the mechanistic crop models. The statistical model is first presented in details, and its value is then illustrated in a case study where the effects of climate change scenarios on different crops are compared. See more from this Division: Special Sessions See more from this Session: Symposium--Perspectives on Climate Effects on Agriculture: The International Efforts of AgMIP